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In this paper, we aim at learning robust and discriminative subspaces from noisy data. Subspace learning is widely used in extracting discriminative features for classification. However, when data are contaminated with severe noise, the performance of most existing subspace learning methods would be limited. Recent advances in low-rank modeling provide effective solutions for removing noise or outliers...
Constrained clustering uses pre-given knowledge to improve the clustering performance. Among existing literature, researchers usually focus on Must-Link and Cannot-Link pairwise constraints. However, pairwise constraints not only disobey the way we make decisions, but also suffer from the vulnerability of noisy constraints and the order of constraints. In light of this, we use partition level side...
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